Skip to main content
SpringerLink
Log in

Analysis on improving the application of machine learning in product development

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

One of the main competitive advantages of an enterprise is its continuous development of new products. The higher the level of product innovation, the more the enterprise is ahead of its competitors. However, products that are more innovative have a higher chance of failure. Traditionally, enterprises use questionnaires, focus groups and customer interviews to understand the expectations and requirements of their customers for specific products; however, if the customers do not fully understand their own needs, they cannot express them fully. Therefore, in order to understand the functional requirements that are expected from their products, some enterprises go directly to the place where their customers are and observe how they use them. Nevertheless, the success rate for new product development is low. To solve this problem, this study uses the machine learning algorithm for the planning and prediction of product R&D. Firstly, the prediction model of a product’s success rate is established by using the random tree algorithm, the bagging algorithm and the SMO algorithm. The model is then used for the planning and prediction of new products. The results show that the model accuracy of the random tree algorithm is 100%, that of the bagging algorithm is 83.33%, and that of the SMO algorithm is 91.67%. The prediction results of the three algorithms are integrated to judge the market’s acceptance of the planned new products. Finally, the new products are improved, and after the improvements have been made, the three algorithms are used to make predictions. As a result, the acceptance of new products by customers has increased significantly. According to this study, machine learning can improve the success rate of new products in the marketplace.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Abu DM, Smoudy A (2019) The role of artificial intelligence on enhancing customer experience. Int Rev Manag Mark 9:22–31

    Google Scholar 

  2. Adams ME, Dougherty D (1998) Enhancing new product development performance: an organizational learning perspective. J Prod Innov Manag 15:403–422

    Article  Google Scholar 

  3. Ayoub J, Zhou F, X, Q, Yang J (2019) Analyzing customer needs of product ecosystems using online product reviews, In: 45th Design Automation Conference, https://doi.org/10.1115/DETC2019-97642.

  4. Bessant J, Francis D (1997) Implementing the new product development process. Technovation 17(4):189–197

    Article  Google Scholar 

  5. Bhuiyan N (2011) A framework for successful new product development. Journal of Industrial Engineering and Management 4(4):746–770

    Google Scholar 

  6. Bijan A, Casper B (2012) New product development and consumer culture: a review. Int J Product Develop 16(1):45

    Article  Google Scholar 

  7. Calantone R, Benedetto CA (2000) Performance and time to market: Accelerating cycle time with overlapping stages. IEEE Trans Eng Manage 47:232–244

    Article  Google Scholar 

  8. Chen CM, Chen L, Gan W, Qiu L, Ding W (2021) Discovering high utility-occupancy patterns from uncertain data. Inf Sci 546:1208–1229

    Article  MathSciNet  Google Scholar 

  9. Chen CM, Huang Y, Wang KH, Kumari S, Wu ME (2020) A secure authenticated and key exchange scheme for fog computing. Enterprise Inf Syst 15:1–16

    Article  Google Scholar 

  10. Chen J, Reilly R, Lynn G (2012) New product development speed: Too much of a good thing? J Prod Innov Manag 29:288–303

    Article  Google Scholar 

  11. Davenport T, Guha A, Grewal D et al (2020) How artificial intelligence will change the future of marketing. J Acad Mark Sci 48:24–42

    Article  Google Scholar 

  12. Ernst H, Hoyer W, Rübsaamen C (2010) Sales, marketing, and research-and development cooperation across new product development stages: implications for success. J Mark 74:80–92

    Article  Google Scholar 

  13. Figueiredo P, Loiola E (2012) Enhancing new product development (NPD) portfolio performance by shaping the development funnel. J Technol Manag Innov 7(4):20–35

    Article  Google Scholar 

  14. Holger Ernst H (2002) Success factors of new product development: a review of the empirical literature. Int J Manag Rev 4(1):1–40

    Article  Google Scholar 

  15. Majava J, Nuottila J, Haapasalo H, Law K (2014) Customer needs in market-driven product development: product management and R&D standpoints. Technol Invest 5:16–25

    Article  Google Scholar 

  16. Nadimpalli M (2017) Artificial intelligence – consumers and industry impact. Int J Econ Manag Sci. https://doi.org/10.4172/2162-6359.1000429

    Article  Google Scholar 

  17. Page A (1993) Assessing new product development practices and performance: establishing crucial norms. J Prod Innov Manag 10:273–290

    Article  Google Scholar 

  18. Patil H, Sirsikar S, Gholap N (2017) Product design and development: phases and approach. Int J Eng Res Technol 6(7):180–187

    Google Scholar 

  19. Ross P (2015) Understanding customer needs. Stat J IAOS 31:291–295

    Article  Google Scholar 

  20. Stobierski T, (2020) Most common types of customer needs to be aware of, Harvard Business School Online's Business Insights Blog. https://online.hbs.edu/blog/post/types-of-customer-needs.

  21. Timoshenko A, Hause J (2019) Identifying customer needs from user-generated content. Marketing Sci 38(1):1

    Article  Google Scholar 

  22. Wang Y, Mo D, Tseng M (2018) Mapping customer needs to design parameters in the front end of product design by applying deep learning. CIRP Ann Manuf Technol. https://doi.org/10.1016/J.CIRP.2018.04.018

    Article  Google Scholar 

  23. Chen X, Li A, Zeng X, Guo W (2015) Huang G (2015) Runtime model based approach to IoT application development. Front Comp Sci 9(4):540–553

    Article  Google Scholar 

  24. Chen X, Lin J, Ma Y, Lin B, Wang H, Huang G (2019) Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model. Sci China Inf Sci 62(11):219101

    Article  Google Scholar 

  25. Chen X, Wang H, Ma Y, Zheng X, Guo L (2020) Self-adaptive resource allocation for cloud-based software services based on iterative QoS prediction model. Futur Gener Comput Syst 105:287–296

    Article  Google Scholar 

  26. Huang G, Liu X, Ma Y, Lu X, Zhang Y, Xiong Y (2019) Programming situational mobile web applications with cloud-mobile convergence: an internetware-oriented approach. IEEE Trans Serv Comput 12(1):6–19

    Article  Google Scholar 

  27. Huang G, Ma Y, Liu X, Luo Y, Lu X, Blake M (2015) Model-based automated navigation and composition of complex service mashups. IEEE Trans Serv Comput 8(3):494–506

    Article  Google Scholar 

  28. Huang G, Xu M, Lin X, Liu Y, Ma Y, Pushp S (2017) Liu X (2017) ShuffleDog: characterizing and adapting user-perceived latency of android apps. IEEE Trans Mob Comput 16(10):2913–2926

    Article  Google Scholar 

  29. Lin B, Huang Y, Zhang J, Hu J, Chen X (2020) Li J (2020) Cost-driven offloading for DNN-based applications over cloud, edge and end devices. IEEE Trans Industr Inf 16(8):5456–5466

    Article  Google Scholar 

  30. Liu X, Huang G, Zhao Q, Mei H, Blake M (2014) iMashup: a mashup-based framework for service composition. Sci China Inf Sci 54(1):1–20

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Management, Yu Da University of Science and Technology, Miaoli, 36143, Taiwan

    Yu-Teng Chang & Hui-Ru Yang

  2. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Chien-Ming Chen

Authors
  1. Yu-Teng Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui-Ru Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chien-Ming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu-Teng Chang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, YT., Yang, HR. & Chen, CM. Analysis on improving the application of machine learning in product development. J Supercomput 78, 12435–12460 (2022). https://doi.org/10.1007/s11227-022-04344-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04344-3

Keywords

Search

Navigation